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Interpretable machine learning for postoperative nausea and vomiting prediction in elderly orthopedic patients: a
Li-Heng Li1, Hao Guo2, Hao Wang3
1Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, China.
BMC Medical Informatics and Decision Making
|May 4, 2026
Summary
A new StackNet model accurately predicts postoperative nausea and vomiting (PONV) risk in elderly patients undergoing orthopedic surgery. This tool provides transparent, calibrated risk assessments for personalized antiemetic prophylaxis.
Area of Science:
- Medical Informatics
- Machine Learning in Healthcare
- Anesthesiology
Background:
- Postoperative nausea and vomiting (PONV) is a common complication impacting patient recovery and satisfaction.
- Accurate risk assessment for PONV in elderly patients is crucial but often lacking in current tools.
- Existing methods for PONV risk prediction may lack transparency and probability calibration.
Purpose of the Study:
- To develop and validate a transparent, highly calibrated machine learning model for predicting PONV risk in elderly orthopedic patients.
- To improve the accuracy of absolute risk assessment for PONV compared to traditional methods.
- To identify key clinical features driving PONV risk through interpretable AI.
Main Methods:
- A StackNet meta-model was developed using hyperparameter optimization across 12 machine learning algorithms.
- A dataset of 1216 elderly patients undergoing hip or knee surgery was partitioned into training, validation, and test sets (7:1:2 ratio).
- Model performance was evaluated using Brier scores, Decision Curve Analysis (DCA), and SHapley Additive exPlanations (SHAP) for interpretability.
Main Results:
- The StackNet model achieved a significantly higher AUC (0.9338) than Logistic Regression (0.7564) for PONV prediction.
- The model demonstrated superior calibration with a Brier score of 0.102.
- SHAP analysis identified preoperative frailty and hemoglobin levels as key predictors of PONV risk.
Conclusions:
- The StackNet framework provides clinically actionable, calibrated risk estimates for PONV in elderly orthopedic patients.
- SHAP interpretability enhances transparency, enabling personalized antiemetic prophylaxis decisions.
- This approach helps avoid unnecessary interventions by providing accurate risk stratification.
